Name
Abstract | ||
|---|---|---|
Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2020.113992 | Expert Systems with Applications |
Keywords | DocType | Volume |
Recommendation system,Knowledge graph,Neural collaborative filtering,Graph convolutional networks,Attention mechanism | Journal | 164 |
ISSN | Citations | PageRank |
0957-4174 | 2 | 0.38 |
References | Authors | |
24 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Sang | 1 | 12 | 2.25 |
| Min Xu | 2 | 3 | 3.44 |
| Shengsheng Qian | 3 | 130 | 19.10 |
| Xindong Wu | 4 | 8830 | 503.63 |